WO2014161061A1 - Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo - Google Patents

Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo Download PDF

Info

Publication number
WO2014161061A1
WO2014161061A1 PCT/CA2013/050266 CA2013050266W WO2014161061A1 WO 2014161061 A1 WO2014161061 A1 WO 2014161061A1 CA 2013050266 W CA2013050266 W CA 2013050266W WO 2014161061 A1 WO2014161061 A1 WO 2014161061A1
Authority
WO
WIPO (PCT)
Prior art keywords
exploration
site data
value
portfolio
probability distribution
Prior art date
Application number
PCT/CA2013/050266
Other languages
English (en)
Inventor
Justin D. ANDERSON
Original Assignee
Anderson Justin D
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anderson Justin D filed Critical Anderson Justin D
Priority to US14/782,264 priority Critical patent/US20160027027A1/en
Priority to PCT/CA2013/050266 priority patent/WO2014161061A1/fr
Publication of WO2014161061A1 publication Critical patent/WO2014161061A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • the invention in general, in one aspect, relates to a method for determining an exploration portfolio value.
  • the method includes receiving a request for the exploration portfolio value targeting an exploration portfolio, where the request comprises exploration site data, obtaining a fiscal regime estimation for the exploration site data, and obtaining a capital expenditure estimation for the exploration site data.
  • the method further includes adjusting the exploration site data using the fiscal regime estimation and the capital expenditure estimation to obtain adjusted exploration site data, generating a probability distribution by executing, using a computer processor, a Monte Carlo simulation on the adjusted exploration site data, and determining the exploration portfolio value from the probability distribution.
  • the invention in general, in one aspect, relates to a system including a computer processor, a fiscal regime abstraction module, a capital expense abstraction module, a Monte Carlo simulation module, and a downside risk adjustment module.
  • the fiscal regime abstraction module is configured to generate a fiscal regime estimation for exploration site data.
  • the capital expense abstraction module is configured to generate a capital expense estimation for the exploration site data.
  • the Monte Carlo simulation module is configured to receive a request for the exploration portfolio value targeting an exploration portfolio, where the request comprises the exploration site data, obtain a fiscal regime estimation from the fiscal regime abstraction module, and obtain a capital expenditure estimation from the capital expense abstraction module.
  • the Monte Carlo simulation module is further configured to adjust the exploration site data using the fiscal regime estimation and the capital expenditure estimation to obtain adjusted exploration site data and generate a probability distribution by a Monte Carlo simulation on the adjusted exploration site data.
  • the downside risk adjustment module is configured to determine the exploration portfolio value from the probability distribution.
  • FIG. 1 shows a system in accordance with one or more embodiments of the invention.
  • FIG. 2 shows a flow diagram in accordance with one or more embodiments of the invention.
  • FIG. 3 shows an example in accordance with one or more embodiments of the invention.
  • FIG. 4 shows a computer system in accordance with one or more embodiments of the invention.
  • embodiments of the invention provide a method and system for generating an exploration portfolio value. Specifically, embodiments of the invention may be used to analyze an exploration portfolio using a probabilistic methodology.
  • Resource extraction companies maintain a portfolio of leased resource sites from which the company extracts the resources. At any given point in time, each resource site is associated with a point in the resource site lifecycle.
  • Prospective sites refer to resource sites that are under the control of the resource extraction company, but often insufficient information exists regarding whether or the amount of resources that may be extracted from the site has been gathered.
  • Exploration sites refer to resource sites that have yet to produce the extractable resource, but have been examined to a sufficient degree regarding the ability of the site to produce resources, and extraction of the resources is expected to begin in the near future.
  • producing sites refer to resource sites that are currently undergoing resource extraction.
  • Embodiments of the present invention use a four-step process to generate an accurate exploration portfolio valuation: first, exploration data from the exploration portfolio is adjusted based on discovery scenarios; second, the exploration data is adjusted based on the fiscal regime of the exploration site; third, a Monte Carlo simulation is executed using the adjust exploration data to generate a probability distribution; and finally, a single valuation is extracted from the resulting probability distribution.
  • Embodiments of the invention may be applicable to portfolios of leases on various kinds of resource extraction sites.
  • resource extraction sites may include, for example, hydrocarbon wells (including oil wells and natural gas deposits), precious metal mines, and underground aquifers (e.g., water wells).
  • leases included in a portfolio may include reservoir extraction leases.
  • Reservoir extraction leases may include various types of leases, such as oil well leases, water well leases, gas well leases, and mine leases.
  • FIG. 1 shows a diagram of a system in accordance with one or more embodiments of the invention.
  • the computing system (100) includes a fiscal regime abstraction module (102), a capital expense (capex) abstraction module (104), a Monte Carlo simulation module (106), and an downside risk adjustment module (108).
  • the computing system In one or more embodiments of the invention, the computing system
  • the computing system (100) is a combination of hardware and software configured to execute the fiscal regime abstraction module (102), capex abstraction module (104), Monte Carlo simulation module (106), and downside risk adjustment module (108).
  • the computing system (100) includes various hardware and software components of a specialized computing systems, such as the computing system shown and described in relation to FIG. 4.
  • the Monte Carlo simulation module (106) is a process or group of processes with functionality to generate a probability distribution. Specifically, the Monte Carlo simulation module (106) receives exploration site data and uses the data to produce a probability distribution expressing the net asset value (NAV) of the exploration sites in terms of their distribution along an x-axis of values. In one or more embodiments of the invention, the Monte Carlo simulation module (106) is executed using two key exploration distributions: the chance of geological success (Pg) and the field size distribution (the P90-P50-P10 of the prospect). In one or more embodiments of the invention, a meaningful distribution of output NAVs requires a high number of iterations (e.g., 5,000- 10,000 iterations). In one or more embodiments of the invention, the data used by the Monte Carlo simulation module (106) is adjusted using the fiscal regime abstraction module (102) and the capex abstraction module (104).
  • the capex abstraction module (104) adjusts the input data for the Monte Carlo simulation module (106) to account for the applicable capital expenditures required to extract the resources from the exploration site. In one or more embodiments of the invention, the capex abstraction module (104) accounts for a wide range of discovery scenarios. In one or more embodiments of the invention, the capex abstraction module (104) uses a regression model to back out a relationship between field size discovered, depth of field discovered, and capital required per proven or probable unit (e.g., barrel). In one or more embodiments of the invention, the capex abstraction module (104) determines the relationship between the above variables from an intensive research process of accumulating data-points for a field's development cost, depth, and reserves.
  • the fiscal regime abstraction module (102) adjusts the input data for the Monte Carlo simulation module (106) to account for the applicable fiscal regimes surrounding the exploration sites.
  • a fiscal regime is the group of financial factors that affect the cost of extracting resources from a exploration site based on the location of the exploration site. For example, the first 5 million barrels of oil produced in a Colombian oil field are exempt from high price royalties, and therefore an exploration site subject to this fiscal regime that was to produce 5 million barrels would have a very different royalty structure than an exploration site governed by the same regime that produced 15 million barrels.
  • the fiscal regime abstraction module (102) defines the fiscal regime in generalized terms, and searches an underlying database of fiscal terms to incorporate the generalized fiscal regime structure specific for that particular discovery and contract into each iteration of the Monte Carlo simulation module (106).
  • the downside risk adjustment module (108) generates a single value or small number of values (e.g., the exploration portfolio value (1 10)) from the probability distribution produced by the Monte Carlo simulation module (106). In one or more embodiments of the invention, the downside risk adjustment module (108) determines the value of a distribution of net present values (NPVs) based on the average market-participant's level of risk aversion. Further detail regarding the downside risk adjustment module (108) is provided in FIG. 2.
  • FIG. 2 shows a flowchart for generating an exploration portfolio valuation in accordance with one or more embodiments of the invention. While the various steps in these flowcharts are presented and described sequentially, one of ordinary skill will appreciate that some or all of the steps may be executed in different orders, may be combined or omitted, and some or all of the steps may be executed in parallel.
  • the computing system receives a request for an exploration portfolio value.
  • the computing system obtains exploration site data for the exploration portfolio.
  • the exploration site data includes data about the location of each resource site, the chance of geological success, and the field size distribution.
  • the exploration site data is adjusted using the fiscal regime abstraction module. In one or more embodiments of the invention, the costs associated with the business aspect of extracting resources from each exploration site is estimated according to the applicable financial factors associated with extraction at a particular location.
  • the exploration site data is adjusted using the capex abstraction module.
  • the exploration site data is modified to account for the costs associated with extracting resources from each exploration site.
  • the capex abstraction module estimates the extraction costs for resource extraction from each exploration site, and adjusts the resource data according to that estimate.
  • Step 218 the Monte Carlo simulation is applied to the adjusted exploration site data and a data point is generated and added to the probability distribution.
  • Step 220 the computing system determines whether enough data points exist in the probability distribution to be statistically significant. If in Step 220, the computing system determines that there is statistically insufficient data points in the probability distribution, then the computing system returns to Step 212 and re-executes the simulation (i.e., executes another iteration).
  • Step 220 the computing system determines whether statistically sufficient data points exist in the probability distribution, then in Step 222, the mean value of the distribution of the NAVs is extracted from the probability distribution. In Step 224, the downside semi -deviation is extracted from the probability distribution. In Step 226, the exploration portfolio value is determined using the mean value of the distribution of the NAVs and the downside semi-deviation. Specifically, in one or more embodiments of the invention, the exploration portfolio value may be determined in terms of an appropriate initial investment ( ) in the company holding the exploration portfolio. i f
  • R is the return on the initial investment and Ep is the mean value of the distribution of NPVs.
  • Rd is the downside semi-deviation case
  • the downside semi-deviation may be calculated from the return on the initial investment (R) and the return on investment for the downside semi-deviation case (Rd) using the following formula:
  • the return on the initial investment (R) may also be calculated using the return required for cash flows not subject to exploration-specific risk (Rf) (i.e., exploration risk-free rate) and the additional return required to compensate for exploration-specific risk (Re):
  • the return required for cash flows not subject to exploration-specific risk is different than the commonly known "risk free rate.”
  • the return required for cash flows not subject to exploration- specific risk is the discount rate that would be applied to the assets absent of exploration risk, but still subject to political, economic, and other risks that would increase the return required for cash flows not subject to exploration- specific risk (Rf) beyond a treasuries-type risk free asset.
  • the initial investment ( ) may be calculated using:
  • FIG. 3 shows an example probability distribution in accordance with one or more embodiments of the invention.
  • the exploration portfolio being evaluated includes 1000 exploration sites subject to 100 different fiscal regimes; each exploration site has is associated with location data and data regarding the amount of resources potentially extractable from the exploration site, dependent upon various environmental and geological factors; each fiscal regime is dependent upon the amount extracted from the exploration site; each data point is calculated as a price-per-stock of the company holding the exploration portfolio; a statistically significant set of data points is at least 10,000 data points.
  • the Sharp ratio (SR) is 35%
  • the return required for cash flows not subject to exploration-specific risk (Rf) is 1 10% and the additional return required to compensate for exploration-specific risk (Re) is 40%.
  • the exploration site data is obtained and adjusted according to a first set of fiscal regime estimates and capex estimates.
  • the Monte Carlo simulation is executed using the adjusted exploration site data, and a data point of $2.10 results.
  • the process is repeated 9,999 more times, each time using generated fiscal regime estimates and capex estimates.
  • the resulting data points are shown in the probability distribution (300).
  • the probability distribution (300) includes a downside value (302 A), a likely value (302B), and an upside value (302C).
  • the downside value (302 A) represents the data points corresponding to the 0 to 25 th percentile likelihood of the estimates used to adjust the exploration data.
  • the likely value (302B) represents the data points corresponding to the 25 th to 75 th percentile likelihood of the estimates used to adjust the exploration data.
  • upside value (302C) represents the data points corresponding to the 75 th to 100 th percentile likelihood of the estimates used to adjust the exploration data.
  • the downside risk adjustment module extracts the mean value of the distribution of the NAVs and the downside semi-deviation from the probability distribution. Assume that that the mean value of the distribution of the NAVs is $7.83, and the downside semi-deviation is $2.41.
  • the following formula is used to determine the appropriate initial investment (//) (exploration portfolio value), per share, of the exploration portfolio:
  • the initial investment (//) is therefore calculated as $2.83 per-share.
  • Embodiments of the invention may be implemented on virtually any type of computing system regardless of the platform being used.
  • the computing system may be one or more mobile devices (e.g., laptop computer, smart phone, personal digital assistant, tablet computer, or other mobile device), desktop computers, servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments of the invention.
  • mobile devices e.g., laptop computer, smart phone, personal digital assistant, tablet computer, or other mobile device
  • desktop computers e.g., servers, blades in a server chassis, or any other type of computing device or devices that includes at least the minimum processing power, memory, and input and output device(s) to perform one or more embodiments of the invention.
  • the computing system (400) may include one or more computer processor(s) (402), associated memory (404) (e.g., random access memory (RAM), cache memory, flash memory, etc.), one or more storage device(s) (406) (e.g., a hard disk, an optical drive such as a compact disk (CD) drive or digital versatile disk (DVD) drive, a flash memory stick, etc.), and numerous other elements and functionalities.
  • the computer processor(s) (402) may be an integrated circuit for processing instructions.
  • the computer processor(s) may be one or more cores, or micro-cores of a processor.
  • the computing system (400) may also include one or more input device(s) (410), such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device. Further, the computing system (400) may include one or more output device(s) (408), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device. One or more of the output device(s) may be the same or different from the input device(s).
  • input device(s) such as a touchscreen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.
  • output device(s) such as a screen (e.g., a liquid crystal display (LCD), a plasma display, touchscreen, cathode ray tube (CRT) monitor, projector, or other display device), a printer, external storage, or any other output device.
  • the computing system (400) may be connected to a network (412) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, mobile network, or any other type of network) via a network interface connection (not shown).
  • the input and output device(s) may be locally or remotely (e.g., via the network (412)) connected to the computer processor(s) (402), memory (404), and storage device(s) (406).
  • LAN local area network
  • WAN wide area network
  • the input and output device(s) may be locally or remotely (e.g., via the network (412)) connected to the computer processor(s) (402), memory (404), and storage device(s) (406).
  • Software instructions in the form of computer readable program code to perform embodiments of the invention may be stored, in whole or in part, temporarily or permanently, on a non-transitory computer readable medium such as a CD, DVD, storage device, a diskette, a tape, flash memory, physical memory, or any other computer readable storage medium.
  • the software instructions may correspond to computer readable program code that when executed by a processor(s), is configured to perform embodiments of the invention.
  • (400) may be located at a remote location and connected to the other elements over a network (412). Further, embodiments of the invention may be implemented on a distributed system having a plurality of nodes, where each portion of the invention may be located on a different node within the distributed system.
  • the node corresponds to a distinct computing device.
  • the node may correspond to a computer processor with associated physical memory.
  • the node may alternatively correspond to a computer processor or micro-core of a computer processor with shared memory and/or resources.

Abstract

La présente invention, selon un aspect, concerne globalement un procédé permettant de déterminer une valeur de portefeuille d'exploration. Selon l'invention, le procédé fait appel à la réception d'une requête de ciblage de valeur de portefeuille d'exploration d'un portefeuille d'exploration, la requête comprenant des données de site d'exploration, à l'obtention d'une estimation de régime financier associée aux données de site d'exploration et à l'obtention d'une estimation de dépense de capital associée aux données de site d'exploration. Le procédé fait en outre appel à la correction des données de site d'exploration à l'aide de l'estimation de régime financier et de l'estimation de dépense de capital pour obtenir des données de site d'exploration corrigées, au fait de générer une distribution des probabilités en appliquant, au moyen d'un processeur informatique, une simulation de Monte Carlo aux données de site d'exploration corrigées, et à la détermination de la valeur de portefeuille d'exploration à partir de la distribution des probabilités.
PCT/CA2013/050266 2013-04-02 2013-04-02 Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo WO2014161061A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US14/782,264 US20160027027A1 (en) 2013-04-02 2013-04-02 System and method of market valuation for resource extraction companies using monte carlo simulation
PCT/CA2013/050266 WO2014161061A1 (fr) 2013-04-02 2013-04-02 Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CA2013/050266 WO2014161061A1 (fr) 2013-04-02 2013-04-02 Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo

Publications (1)

Publication Number Publication Date
WO2014161061A1 true WO2014161061A1 (fr) 2014-10-09

Family

ID=51657346

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2013/050266 WO2014161061A1 (fr) 2013-04-02 2013-04-02 Système et procédé d'évaluation boursière d'entreprises d'extraction de ressources au moyen d'une simulation de monte carlo

Country Status (2)

Country Link
US (1) US20160027027A1 (fr)
WO (1) WO2014161061A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9881339B2 (en) * 2012-12-18 2018-01-30 Johnathan Mun Project economics analysis tool

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070124227A1 (en) * 1999-11-26 2007-05-31 Algorithmics International Corp. System and method for trading off upside and downside values of a portfolio

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835893B2 (en) * 2003-04-30 2010-11-16 Landmark Graphics Corporation Method and system for scenario and case decision management
US7546228B2 (en) * 2003-04-30 2009-06-09 Landmark Graphics Corporation Stochastically generating facility and well schedules
US9228415B2 (en) * 2008-10-06 2016-01-05 Schlumberger Technology Corporation Multidimensional data repository for modeling oilfield operations

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070124227A1 (en) * 1999-11-26 2007-05-31 Algorithmics International Corp. System and method for trading off upside and downside values of a portfolio

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Case Study: Oil and Gas Exploration and Production'' (Hoye", 21 October 2006 (2006-10-21), Retrieved from the Internet <URL:https://web.archive.org/web/20061021125734/http://www.realoptionsvaluation.com/attachments/Case_Study_Oil_and_Gas_Exploration_and_Production.pdf> *
ANDERSON ET AL.: "Oil and Gas Valuation Methods with a Focus on Monte Carlo Analysis", 15 March 2013 (2013-03-15), Retrieved from the Internet <URL:http://www.youtube.com/watch?v=70QSxeB9joY> *

Also Published As

Publication number Publication date
US20160027027A1 (en) 2016-01-28

Similar Documents

Publication Publication Date Title
Özmen et al. RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty
Gülen et al. Well economics across ten tiers in low and high Btu (British thermal unit) areas, Barnett Shale, Texas
US20140310071A1 (en) Physically-based financial analysis and/or forecasting methods, apparatus, and systems
Evatt et al. Mineral reserves under price uncertainty
US20210272195A1 (en) Instant Lending Decisions
WO2019231772A1 (fr) Systèmes et procédés de détection de flux de transaction automatisée de cryptomonnaie
WO2015171799A1 (fr) Systèmes, appareil et procédés d&#39;analyse géographique basée sur la physique de pétrole, de gaz, d&#39;eau et d&#39;autres fluides
CN110472895B (zh) 财务系统风控方法、装置、计算机设备和存储介质
Zhao et al. How the removal of producer subsidies influences oil and gas extraction: A case study in the Gulf of Mexico
Adeogun Developing a framework for maximizing marginal oil and gas field economics
WO2019095569A1 (fr) Procédé d&#39;analyse financière basé sur un événement financier et économique sur un microblogue, serveur d&#39;applications, et support de stockage lisible par ordinateur
US20230418843A1 (en) Methods and systems for classifying database records by introducing time dependency into time-homogeneous probability models
Lin et al. Modeling epistemic subsurface reservoir uncertainty using a reverse Wiener jump–diffusion process
US20160027027A1 (en) System and method of market valuation for resource extraction companies using monte carlo simulation
US20150134564A1 (en) Predicting economic conditions
US10475131B1 (en) Methods, systems and computer program products for calculating an estimated result of a tax return
Tugan et al. Selection of Best Reserves Estimation Methodology to Quantify and Reduce the Uncertainty–Accompanied by Çayirdere Gas Field Case Study
Onwuka et al. Evaluating the impact of depreciation methods and production decline patterns on deepwater economics: A case study of Nigeria
Sarsar et al. Empirical analysis of the economic complexity boost on the impact of energy transition on economic growth: A panel data study of 124 countries
Harding Application of stochastic prospect analysis for shale gas reservoirs
Lei et al. Price stress testing in offshore oil field development planning
CN113837648A (zh) 企业关联性分析方法、关联企业推荐方法和装置
CN112053236A (zh) 风险信息识别方法、装置、计算设备和介质
Moridis et al. Gaussian Quadrature Accurately Approximates the Relative Weights of Each Reserves Category of the PRMS Matrix Through a Cumulative Distribution Function
Aliaga et al. Development of natural gas assets in frontier offshore areas under challenging technical and economic environment-A project finance with real options approach

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 13881092

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 14782264

Country of ref document: US

122 Ep: pct application non-entry in european phase

Ref document number: 13881092

Country of ref document: EP

Kind code of ref document: A1